Computer-implemented system and method for efficiently reducing transcription error during a call

ABSTRACT

A computer-implemented system and method for efficiently reducing transcription error during a call is provided. A group of similar questionable utterances is monitored. Each questionable utterance is assigned a transcribed value and is selected from a different call. A sample of the similar questionable utterances is selected from the group and provided to a human transcriber. A further transcribed value for each of the similar questionable utterances in the sample is received from the human transcriber. The further transcribed values for the similar questionable utterances in the sample are compared. Each of the remaining similar questionable utterances in the group are provided to the human transcriber when the compared further transcribed values of the similar questionable utterances in the sample are different.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.14/636,087, filed Mar. 2, 2015, pending, which is a continuation of U.S.Pat. No. 8,972,261, issued Mar. 3, 2015, which is a continuation of U.S.Pat. No. 8,645,136, issued Feb. 4, 2014, the disclosures of which areincorporated by reference.

FIELD

The present invention relates in general to speech recognition, inparticular, to a system and method for efficiently reducingtranscription error during a call.

BACKGROUND

Automated speech recognition is commonly used in call centers to convertvoice signals from callers into text. Generally, a call or voicerecording is received into the call center and speech is obtained. Thespeech is input into an automated speech recognition system, whichparses the speech into short segments and assigns phonemes to thesegments. The phonemes are analyzed and compared to a grammar of knownwords, phrases, and sentences to provide text values for the speech.

Once converted, the text can be used to store a record of a call, toidentify characteristics of the call, or as a confirmation of the call.Speech recognition is also widely used in other fields, including thelegal field for court reporting and dictation, and the medical field.The benefits of automated speech recognition include a reduction in thecost of employees required to manually transcribe voice messages and anincrease in transcription speed. However, a lack of transcriptionaccuracy is a barrier to widespread use of automated speech recognition.

A conventional approach using automated speech recognition and manualtranscription has been implemented as an attempt to address and improvetranscription accuracy. Generally, a voice message is first transcribedvia automated speech recognition. Subsequently, an accuracy threshold isapplied to the transcribed voice message. If the accuracy of thetranscribed message is above the threshold, the transcribed voicemessage is provided to a user or stored. Whereas, if the accuracy of thetranscribed message is below the threshold, the entire voice message istransmitted to a human transcriber for manual transcription. Duringmanual transcription, each voice utterance in the voice messagetransmitted to the human transcriber is separately processed, which canbe expensive and time consuming.

In large call centers, hundreds or thousands of calls can be receivedwithin a relatively short time period. During this time period commonutterances are received into the call center from different callers asvoice. According to the conventional approach described above, if thetranscription of the voice message fails to meet a threshold accuracy,the entire voice message is then manually transcribed, which can becostly and time consuming. Thus, the conventional approach fails toreduce error by identifying similar utterances during a specified timeperiod, manually transcribing at least one of the utterances, and thenassigning the transcribed value to the remaining similar utterances.

Therefore, there is a need for providing efficient and cost effectiveapproaches for reducing transcription error via a hybrid of automatictranscription and manual transcription. Preferably, the approach wouldinclude a reduction in the amount of manual transcription required byidentifying similar utterances, manually transcribing at least one ofthe similar utterances, and assigning the manually transcribed value tothe remaining similar utterances.

SUMMARY

A system and method for reducing transcription error is provided. Avoice stream, such as a live voice stream or a recording, is collectedand parsed into speech utterances. The voice stream is transcribed byassigning a transcription value and confidence score to each utterance.A threshold is applied to the confidence scores and only thoseutterances with confidence scores below the threshold are selected asquestionable utterances. The questionable utterances have a higherlikelihood of being associated with an incorrect transcription value andshould be further analyzed. One of the questionable utterances isselected and a pool of similar questionable utterances from other voicestreams is generated. Subsequently, a sample is selected from the pooland each utterance in the sample is manually transcribed. If a commontranscribed value is assigned to each utterance in the sample, thecommon transcribed value is then assigned to the remaining questionableutterances in the pool and incorporated into respective transcribedmessages. Otherwise, the remaining questionable utterances are eachmanually transcribed.

One embodiment provides a computer-implemented system and method forefficiently reducing transcription error during a call. A group ofsimilar questionable utterances is monitored. Each questionableutterance is assigned a transcribed value and is selected from adifferent call. A sample of the similar questionable utterances isselected from the group and provided to a human transcriber. A furthertranscribed value for each of the similar questionable utterances in thesample is received from the human transcriber. The further transcribedvalues for the similar questionable utterances in the sample arecompared. Each of the remaining similar questionable utterances in thegroup are provided to the human transcriber when the compared furthertranscribed values of the similar questionable utterances in the sampleare different.

Still other embodiments of the present invention will become readilyapparent to those skilled in the art from the following detaileddescription, wherein is described embodiments of the invention by way ofillustrating the best mode contemplated for carrying out the invention.As will be realized, the invention is capable of other and differentembodiments and its several details are capable of modifications invarious obvious respects, all without departing from the spirit and thescope of the present invention. The drawings and detailed descriptionare to be regarded as illustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram showing a system for hybrid voicetranscription, in accordance with one embodiment.

FIG. 2 is a functional block diagram showing, by way of example, thehybrid voice transcription system of FIG. 1 as incorporated into a callcenter.

FIG. 3 is a process flow diagram showing a method for hybrid voicetranscription, in accordance with one embodiment.

FIG. 4 is a process flow diagram showing, by way of example, a methodfor building pools of similar utterances for use in the process of FIG.3.

FIG. 5 is data flow diagram showing, by way of example, similaritymetrics for use in the process of FIG. 4.

FIG. 6 is a data flow diagram showing, by way of example, metrics forobtaining a sample from the pools of FIG. 4.

FIG. 7 is a block diagram showing, by way of example, an initialtranscribed message.

FIG. 8 is a block diagram showing, by way of example, the transcribedmessage of FIG. 7 with identification values representing transcribedvalues for questionable utterances.

FIG. 9 is a block diagram showing, by way of example, a pool of similarquestionable utterances.

FIG. 10 is a block diagram showing, by way of example, a transcribedutterance value chart.

DETAILED DESCRIPTION

Automated speech recognition provides fairly quick and convenienttranscription of voice to text that is frequently used in call centers,as well as the legal and medical industries to reduce costs and increaseefficiency. However, conventional systems for automated speechrecognition often times achieves the ability to reduce costs andincrease efficiency at the expense of transcription accuracy. Quick,easy, and accurate reduction of any transcription errors is desiredduring transcription. Reducing transcription errors using hybrid voicetranscription provides manual review and transcription for at least oneutterance from a voice stream and assigning a transcribed value tosimilar utterances from other voice stream.

Hybrid voice transcription requires a system involving automated voicerecognition with human intervention. FIG. 1 is a functional blockdiagram showing a system 10 for hybrid voice transcription, inaccordance with one embodiment. A speech recognition server 11 isinterconnected to conventional telephone handsets 12 through Plain OldTelephone Service (POTS) 13, portable handsets 19 through cellular andsatellite telephone service 18, and desktop 14 or portable 16 computers,including VoIP clients, Internet clients and Internet telephony clients,through an internetwork 17, such as the Internet. The speech recognitionserver 11 receives a voice stream 22 from a user via the conventionaltelephone handsets 12, portable handsets 19, and computers 14, 16. Thevoice stream can be provided as a real-time voice stream or as arecorded voice message. The speech recognition server 11 includes aparser 24, scorer 25, pool generator 26, and message generator 27.

The parser 24 identifies the voice stream 22 received on the speechrecognition server 11 and parses the stream into utterances, which caninclude word-level tokens, n-grams, raw terms, noun phrases, andsentences. Other types of utterances are possible. The scorer 25analyzes the utterances and assigns an initial transcribed value andconfidence score to each utterance. The initial transcribed values canbe selected from a grammar 21 that is stored in a database 20 coupled tothe speech recognition server 20. The transcribed values are combined togenerate a transcribed message 23 for the voice stream. The poolgenerator 26 identifies those utterances in the initial transcribedmessage with a low confidence score and attempts to group thoseutterances with similar utterances into a pool. Additionally, the poolgenerator 26 can select a sample of utterances from the pool andtransmit the sample to at least one human transcriber 15 for furtherprocessing and analysis. The human transcriber 15 can listen to eachutterance in the sample and confirm the initial transcribed value orassign an additional transcribed value. In a further embodiment, theadditional processing of the sample is performed automatically, ratherthan manually. The human transcriber can be an employee of a callcenter, a third party transcription service, or other business.

The manually transcribed value for each utterance in the sample aretransmitted to the speech recognition server 11 and compared. If themanually transcribed values differ, the remaining similar utterances inthe pool are also manually transcribed. However, if all manuallytranscribed values are the same, the common transcribed value is alsoassigned to the remaining similar utterances in the pool. Subsequently,the message generator 27 incorporates the manually assigned or affirmedtranscribed value into the transcribed message for the voice stream 22from which the utterance was obtained. The transcribed message 23 andcorresponding voice stream 22 can be stored in the database 20 forfurther reference. Further, the transcribed message 23 can be providedto the user.

The handsets 12, 19, computers 14, and speech recognition server 11 eachinclude components conventionally found in general purpose programmablecomputing devices, such as a central processing unit, memory,input/output ports, network interfaces, and non-volatile storage,although other components are possible. Moreover, other informationsources in lieu of or in addition to the servers, and other informationconsumers, in lieu of or in addition to the handsets and computers, arepossible.

Additionally, the handsets 12, 19, computers 14, 16, and speechrecognition server 11 can each include one or more modules for carryingout the embodiments disclosed herein. The modules can be implemented asa computer program or procedure written as source code in a conventionalprogramming language and is presented for execution by the centralprocessing unit as object or byte code. Alternatively, the modules couldalso be implemented in hardware, either as integrated circuitry orburned into read-only memory components. The various implementations ofthe source code and object and byte codes can be held on acomputer-readable storage medium, such as a floppy disk, hard drive,digital video disk (DVD), random access memory (RAM), read-only memory(ROM) and similar storage mediums. Other types of modules and modulefunctions are possible, as well as other physical hardware components.

The hybrid voice transcription system 10 can be incorporated for use inother systems, including a call center. FIG. 2 is a functional blockdiagram showing, by way of example, the hybrid voice transcriptionsystem of FIG. 1 as incorporated into a call center 30. The call center30 includes a message server 31 and a speech recognition server 34operatively interconnected over a network 33, such as an internalnetwork or the Internet. One or more human transcribers 32 a-c can alsobe interconnected to the message server 31 and speech recognition server34 over the network 33. The human transcribers 32 a-c can be employeesof the call center 30, as well as employees of a third party serviceprovider. In a further embodiment, the human transcribers 32 a-c can bereplaced with an automated system for further processing the transcribedmessages, including assigning further transcription values to one ormore utterances. The network infrastructure can be either wired orwireless and, in one embodiment, is implemented based on theTransmission Control Protocol/Internet Protocol (TCP/IP) networkspecification, although other types or combinations of networkingimplementations are possible. Similarly, other network topologies andarrangements are possible.

Incoming customer calls are received through a call interface 35, whichis operatively coupled to the message server 31 to provide access to atelephone voice and data network. In one embodiment, the call centerinterface 35 connects to the telephone network over a T-1 carrier line,which can provide up to 24 individual channels of voice or data trafficprovided at 64 kilobits (Kbits) per second. Other types of telephonenetwork connections are possible. Once received, the message server 31sends streamed audio data from the customer call as a voice stream 36 tothe speech recognition server 34, which performs automatic speechrecognition by parsing the voice stream into utterances and assigning atranscribed value and confidence score to each utterance.

An accuracy threshold is applied to the confidence scores of theutterances and those utterances with confidence scores below thethreshold are selected as questionable utterances. Questionableutterances can include those utterances for which the initialtranscribed value has a threshold likelihood of being an incorrectrepresentation of that utterance. Higher confidence scores indicate ahigher likelihood that an initial transcribed value assigned to anutterance is an accurate transcription of that utterance. Whereas, lowerconfidence scores indicate a lower likelihood that an initialtranscribed value is an accurate transcription of an utterance. Thevalue of the confidence score assigned reflects the degree of likelihoodthat an initial transcribed value reflects the corresponding utterance.One or more of the questionable utterances can be transmitted forfurther analysis, such as by a human transcriber, for confirming theinitial transcribed value or for assigning a further transcribed value.Hybrid voice transcription will be discussed below with respect to acall center. However, hybrid voice transcription can also beincorporated for use in other systems for fields, such as the legal ormedical fields.

As briefly described above, hybrid voice transcription can occur througha sequence of phases. FIG. 3 is a process flow diagram showing a method40 for hybrid voice transcription, in accordance with one embodiment.Audio data is received from a caller as a voice stream, which is parsedinto utterances (block 41). Each utterance includes tokens, such aswords, n-grams, raw terms, noun phrases, and sentences. Other types ofutterances are possible. A transcribed value and confidence score areinitially assigned to each utterance via automated speech recognition(block 42). The initial transcribed value can include a textrepresentation of the corresponding voice utterance and can beautomatically selected from a grammar, which includes a list of commontranscribed values that may occur in the voice stream during a call. Oneor more grammars can be selected based on characteristics of a caller,including geographic location, age, interests, and network.Additionally, the confidence score provides a measure of certainty thatthe transcribed value accurately represents the voiced utterance. Anaccuracy threshold is applied to the confidence scores of the utterancesand those utterances with confidence score below the threshold areselected for further analysis (block 43). The threshold can beautomatically determined or manually set, such as by an employee of acall center, including a manager. At least one of the questionableutterances is selected and attempts to build a pool of similarquestionable utterances for the selected questionable utterance are made(block 44). If a pool is formed, one or more of the similar questionableutterances can be selected as a sample for further analysis. Theinformation from the further analysis can be used to confirm theaccuracy of or change the initial transcribed value assigned to theremaining similar questionable utterances in the pool without having tobe individually reviewed by a manual transcriber.

Building the pool of similar questionable utterances can includeidentifying a predetermined number of similar questionable utteranceswithin a predetermined timed frame. Identifying similar questionableutterances for inclusion in a pool is further discussed below withreference to FIG. 4. If an appropriate sized pool of similarquestionable utterances are not identified within the predetermined timeframe, each of the similar questionable utterances are transmitted to ahuman transcriber (block 48) for confirming the initial assignedtranscribed value or for assigning a further transcribed value. However,if an appropriate sized pool of similar questionable utterances isidentified within a predetermined time period, a sample of thequestionable utterances is selected (block 45). The sample can beselected randomly, specifically, or via a combination of random andspecific parameters. Selecting a sample is further described below withreference to FIG. 6.

Once selected, the sample of similar questionable utterances istransmitted to a human transcriber for confirming the assignedtranscribed values for or assigning further transcribed values to eachutterance in the sample. The questionable utterances in the sample canbe transmitted to the same human transcriber or alternatively, todifferent human transcribers. Each similar questionable utterance in thesample is analyzed to identify the correct transcribed value associatedfor that utterance. Upon receipt from the human transcriber, theconfirmed or newly assigned transcribed values are compared. If allquestionable utterances in the sample are associated with the sametranscribed value (block 46), the common transcribed value is thenconfirmed for or assigned (block 47) to all the questionable utterancesin the pool from which the sample was selected. The common transcribedvalue is confirmed for those questionable utterances having initialtranscribed values that match the common transcribed value. The commontranscribed value is assigned to the questionable utterances when theinitial transcribed value is different from the common transcribedvalue. If no common transcribed value is provided for the similarquestionable utterances in the sample, each remaining questionableutterance in the pool is then transmitted to the human transcriber(block 48) for further review, such as for confirmation of the initialtranscribed value or assignment of a further transcribed value.

The transcribed values confirmed or assigned by the human transcribercan be incorporated into the initial transcribed message (block 49) forthe corresponding utterance, which can be provided to the caller (block50). In a further embodiment, the transcribed value and associatedutterance can be stored with the grammar or used to generate a furthergrammar.

During hybrid voice transcription, a subset of similar questionableutterances can be identified and transmitted for further processing toincrease the accuracy of fully automated speech recognition systems,while maintaining efficiency and reducing cost. The subset of similarquestionable utterances can be generated as a pool from which a samplecan be selected. FIG. 4 is a process flow diagram showing, by way ofexample, a method 60 for building pool of similar questionableutterances for use in the process of FIG. 3. Questionable utterances areidentified (block 61) from an initial transcribed message based on aconfidence score for a corresponding initial transcribed value assignedto each utterance. In one embodiment, the questionable utterancesinclude those utterances with initial transcribed values havingconfidence scores that fall below a predetermined accuracy threshold. Atleast one of the questionable utterances is selected and monitored for apredetermined amount of time during which questionable utterances invoice streams from other calls are watched. The other questionableutterances that are similar to the selected questionable utterance areidentified (block 62) and grouped with the questionable utterance (block63).

The similarity of two or more questionable utterances can be determinedbased on similarity metrics. FIG. 5 is a data flow diagram 70 showing,by way of example, similarity metrics 71 for use in the process of FIG.4. The similarity metrics 71 include factors for determining whether twoor more questionable utterances are similar, such as by the initialtranscribed values 72, confidence scores 73, range of similarity 74shared between the at least two transcribed values, and callcharacteristics 75. For example, using the initial transcribed valuesimilarity factor, if two utterances have each been assigned the sameinitial transcribed values, the two utterances can be determined to besimilar. Alternatively, even if the initial transcribed values aredifferent, they may still considered to be similar if they fall within asimilarity range of transcribed values. The identification of similarquestionable utterances can also be based on the confidence scoreassociated with the initial assigned transcribed values. For example, iftwo utterances are assigned a common initial transcribed value, the twoutterances may only be considered as similar if the associatedconfidence scores fall above a confidence score threshold or within aparticular range of confidence.

Also, a range of similarity 74 can also be applied to determine whetherthe initial transcribed value or the corresponding confidence score fora questionable utterance are within the predetermined bounds fordetermining similarity with another questionable utterance. As well,call characteristics, such as geographic area, age, and affiliations canbe used as factors in determining whether two questionable utterancesare similarly related. Further examples of determining similarity basedon the similarity metrics are provided below with reference to FIG. 8.

During the identification of similar questionable utterances, at leasttwo factors are monitored to determine whether the grouped similarquestionable utterances form a pool from which a sample can be selectedfor further analysis. The factors include a time threshold (block 64)and a size threshold (block 65). Regarding the time limit threshold, anautomatically determined or predetermined time period is selected duringwhich a pool of similar questionable utterances must be grouped. Ifgrouped within the selected time period (block 64), the similarutterances are recognized as a pool from which a sample of thequestionable utterances can be selected. However, if not grouped withinthe time period, each similar questionable utterance identified istransmitted to the human transcriber for individual analysis. The timeperiod can be an absolute time or a period of time that covers a range.Additionally, the size threshold can be applied to require that agrouping of similar questionable utterance reaches a certain size to beconsidered a pool. The size threshold can be automatically determined orpredetermined to establish a particular number of similar questionableutterances that must be grouped to form a pool from which a sample canbe selected. If the size threshold is satisfied (block 65), one or moreof the similar questionable utterances can be selected as a sample forfurther analysis, whereas each of the similar questionable utterancesare transmitted for further analysis when the size threshold is notsatisfied.

Setting the size and time thresholds can be determined based on a typeof voice stream, accuracy of transcription required, and type of serviceagreement signed. The type of voice stream can include a real-time voicestream or a recorded voice message. When the utterances are beingprovided in a real time voice stream during a live call, the time foridentifying similar questionable utterances should be shorter since thecaller is waiting on the call. In one embodiment, the time limit can beset at three to thirty seconds for a real time call. However, when theutterances are provided in a recorded voice message, the time toidentify similar questionable utterances can be longer. In oneembodiment, the time limit can be set at 30 minutes for a recorded voicemessage. With respect to live calls, the size threshold can be setlower, such that fewer similar utterances are required to form a pool.Additionally, for recorded calls, the size threshold can be set highersince more time is provided to identify similar questionable utterances.

Further, the size threshold for a pool can be determined based on anaccuracy of the transcription required. For example, a larger poolprovides more accurate results, whereas a smaller pool provides morerelaxed results. High accuracy transcription may be required in courtreporting and lower accuracy may be required for simple consumer callsthat request information.

In one embodiment, the time limit is monitored and upon expiration, theidentified similar utterances are analyzed to determine whether the sizethreshold is satisfied. If the pool fails to satisfy either the sizedthreshold or the time limit, no pool of the similar questionableutterances is formed. However, if a pool of similar questionableutterances satisfies the size threshold and is formed within thepredetermined time limit, the pool is assigned as a pool for use inhybrid voice transcription.

Once the pool of similar questionable utterances is generated, a samplecan be selected for sending to the human transcriber. FIG. 6 is a dataflow diagram 80 showing, by way of example, metrics for obtaining asample 81 from the pools of FIG. 4. A sample can be obtained from a poolbased on random sampling 82, specific sampling 83, or a combination ofrandom and specific sampling 84. During a random sampling, one or morequestionable utterances are selected from the pool at random. The numberof selected questionable utterances can be automatically determined ordetermined by a user. During specific sampling, particular questionableutterances are selected as representative of the sample based onfactors, including confidence score, characteristics of the caller,subject matter, and noise, as well as other factors. A combination ofrandom sampling and specific sampling can also be used. For instance, apredetermined number of questionable utterances can be selected throughrandom sampling, while a further set of questionable utterances can beselected through specific sampling to generate a complete sample. In afurther embodiment, random sampling can be used to identify a sample ofquestionable utterances, which can be further refined through specificsampling. Other combinations of random sampling and specific samplingare possible.

The size of a sample can be determined based on an accuracy of thetranscription required. For example, a larger sample provides moreaccurate results, whereas a smaller sample provides more relaxedresults. High accuracy transcription may be required in legalproceedings, such as court reporting, while lower accuracy results maybe sufficient for simple consumer calls or other activities.

FIG. 7 is a block diagram showing, by way of example, a transcribedmessage 90. The transcribed message 90 includes a message details box 91and a text box 92. The message details box 91 includes fields formessage characteristics, including, for example, message identification,date, time, sender, and recipient. Other message characteristics arepossible, such as identification of related messages or time required totranscribe the message.

The message identification field can include a number, letter, or symbolfor representing a transcribed message for a particular voice stream.Corresponding voice streams and transcribed messages can have the sameor different identification values, as well as related identificationvalues. The date field can include the date on which the original voicestream was received, the date on which the voice stream was transcribed,or the date on which the transcribed message was provided to a caller.In one embodiment, the date for receiving the voice stream, fortranscribing the voice stream, and for providing the transcribed messageis the same. Similar to the date field, the time field can include thetime at which the original voice stream was received, the time at whichthe voice stream was transcribed, or the time at which the transcribedmessage was provided to a caller.

The sender field can include the name of an individual, business entity,or other sender. Additionally, other sender identifiers can be used,such as an identification number for or telephone number of the sender.Similarly, the recipient field can also include the name of anindividual, a business entity, or other recipient, as well as otheridentifiers, such as an identification number or telephone number. Otheridentifiers are possible. As well, the recipient field can include oneor more recipients, each identified by the same or different types ofidentifiers.

In the text box 92, an initial message 94 transcribed via automatedspeech recognition can be provided. In the transcribed message 94, eachutterance in the corresponding voice stream is assigned a transcribedvalue, which is represented as text. The transcribed message 94 in thetext box reads:

Good Morning “hector” - it's Jeff - just about “eigtam” to follow upwith you on - the - I “ATT” awards nomination - I don't have “qcall”back from - “” I think she'd asked you to get involved - I've “gaaata”?get that done todayEach transcribed utterance can be associated with a confidence score(not shown), which reflects a level of certainty as to whether thetranscribed value accurately reflects the voiced utterance.

An accuracy threshold can be applied to the confidence scores of eachutterance in the initial transcribed message 94 to identify thoseutterances with transcribed values having confidence scores below thethreshold as questionable utterances. If above the threshold, thetranscribed values assigned to the utterances can be considered accurateor fairly accurate. Meanwhile, the questionable utterances can have alikelihood of being associated with an incorrect transcribed value. Theconfidence score reflects the likelihood of accurate transcription. Inthe transcribed message 94, the questionable utterances can beidentified by quotation marks. In place of, or in addition to, thequotation marks, the questionable utterances can also be displayed byhighlighting one or more utterances, displaying one or more utterancesin an utterance box, and through different font sizes and styles.

The transcribed values for the questionable utterances can each berepresented by identification values for use in identifying similarquestionable utterances. FIG. 8 is a block diagram showing, by way ofexample, a transcribed message with identification values representingtranscribed values for questionable utterances. Each transcribed valueassociated with a questionable utterance in the transcribed message isreplaced with an identification value and a confidence score for thetranscribed value. The identification value can include a number, code,or symbol. Each transcribed value is assigned a different identificationvalue; however, the same or similar transcribed values may be assignedthe same identification value. Other types and assignments ofidentification values are possible. The questionable utterances can bedisplayed with quotation marks or other displays, includinghighlighting, font size, and utterance boxes. For instance, in thetranscribed message, the transcribed values having a confidence scoreabove the threshold can be represented as text, which is highlighted ina first color, and the questionable utterances can be represented astext that is highlighted in a second color.

One or more of the questionable utterances can be selected and watchedfor a predetermined amount of time during which attempts are made tolocate similar questionable utterances. Returning to the above example,a threshold of 80% is applied to the transcribed values in the message.Those utterances associated with transcribed values having a confidencelevel lower than the threshold are selected as questionable utterances.Thus, the utterances associated with transcribed values identified bythe values “111100,” “X2000,” “Y4359,” “X45600” “333200,” “222200,” and“X3450” are selected as questionable utterances. Further, thequestionable utterance associated with the transcribed value “gaaata” isselected. “Gaaata” is represented by the identification value “X3450”with a confidence score of 65%. The corresponding questionable utteranceis then watched for a time period of 30 minutes during which attempts tolocate similar questionable utterances are made.

The located similar questionable utterances form a pool with theselected questionable utterance. FIG. 9 is a block diagram showing, byway of example, a pool of similar questionable utterances 110. Asdescribed above with reference to FIG. 4, the pool can be generated froma predetermined number of similar questionable utterances, which areidentified within a predetermined amount of time. Each questionableutterance 111 in the pool 110 is represented by an identification value112, which is based on a transcribed value, and a confidence score 113.The similarity between questionable utterances can be determined basedon similarity metrics, including an assigned transcribed utterance,confidence score, range of similarity, and call characteristics.However, other similarity metrics are possible. Returning to the aboveexample, the transcribed value “gaaata” is watched while a search isperformed for similar questionable transcribed utterances. A furtherquestionable utterance with the transcribed value “gotta” is identified.The further transcribed value is assigned an identification value of“X3451” with a confidence score of 55%. A similarity comparison of thetranscribed values is performed between the two questionable utterancesbased on one or more of the similarity metrics. For instance, in thisexample, the applicable similarity metrics include the assignedtranscribed value, confidence score, and range of similarity. Thetranscribed value “gaaata” is associated with the identification valueof “X3450,” while “gotta” has an identification value of “X3451.” Theassigned transcribed value similarity metric requires that theidentification value of two or more utterances fall within a range offive. In this case, the identification values of the two utterancesdiffer by a value of one. Accordingly, the two utterances are consideredsimilar.

For the confidence score comparison, an automatic threshold or apredetermined threshold can be used to identify similar questionableutterances. For instance, a confidence score threshold of 35% isselected and those utterances with similar identification values, asdescribed above, that have confidence scores over 35% can be selected assimilar. Further, use of the bounded range similarity metric requiresthat the identification value, confidence score, or both theidentification value, and confidence scores fall within a predeterminedbounded range, including an upper and lower limit for two or moreutterances to be considered similar.

Also, call characteristics can be used as factors for determiningsimilarity between two or more questionable utterances, including anyaspect of a call or the caller, such as location, topic, emotion,accent, gender, and age. Other call characteristic factors are possible.The call characteristics can be provided by a caller or obtained from ananalysis of the call, such as by caller ID.

In one example, an utterance with a transcribed value of “U-dub” isparsed from a voice stream. A caller in Oregon generated the voicestream during a call to a call center. The location of the caller can beidentified via caller ID or directly provided by the caller. Alone, thetranscribed value “U-dub” could refer to the University of Washington orthe University of Wisconsin. Consideration the location information canhelp distinguish the correct reference for “U-dub.” A caller located inthe Pacific Northwest, such as Washington or Oregon, is more likely torefer to the University of Washington. However, a caller in the Midwest,such as Wisconsin is more likely to use “U-dub” as a reference to theUniversity of Wisconsin. Accordingly, in the current example, “U-dub,”will have a higher similarity to other references to the University ofWashington, rather than references to the University of Wisconsin.Additionally, utterances with a transcribed value of “U-dub” from acaller in Oregon and caller in Wisconsin would not be consideredsimilar.

Additionally, in a further example, age can be used as a factor forgrouping similar utterances that represent slang. “Scrilla” is a term ofslang that is used by youth and young adults to refer to money. Thus,knowledge of a caller's age can help determine whether the transcribedvalue for “scrilla” is a single term or a portion of a term, such asgorilla. If “scrilla” is identified in a voice stream from a caller thatis 18 years old, the term likely refers to money, whereas, from a callerthat is 59 years old, “scrilla” is likely to be an incomplete orinaccurate transcription of the utterance. Accordingly, the twotranscribed utterances would not be considered similar.

Once a pool of similar questionable utterances has been formed, a sampleof the questionable utterances can be selected for transmitting to oneor more human transcribers, as described above in detail with referenceto FIG. 6. Each questionable utterance in the sample is individuallytranscribed via manual transcription. If the same transcribed value isassigned to each questionable utterance in the sample, the commontranscribed value is assigned to each of the remaining questionableutterances in the pool. When the common transcribed value is differentthan the initial transcribed value, the assigned common value replacesthe initially assigned transcribed value. The transcribed message towhich the questionable utterance belongs is also updated to reflect thecommon transcribed value. However, if the common transcribed value isthe same as the initially transcribed value, the initially transcribedvalue is confirmed as being a correct representation of the associatedutterance. If different transcribed values are assigned to thequestionable utterances in the sample, each of the remainingquestionable utterances in the pool are manually transcribed.

The manually assigned transcribed values can be stored for furtherreference. FIG. 10 is a block diagram showing, by way of example, atranscribed value chart 120. The transcribed value chart 120 includesquestionable utterances identified for at least one voice stream, whichare each represented by an identification value and confidence score123. The identification value and confidence score can be assignedduring automated speech recognition 121. Additionally, the transcribedvalue chart 120 includes the actual transcribed values 122, which caninclude the transcribed values 124 provided by the human transcriber orthe transcribed value provided by automated speech recognition. Theactual transcribed values have been verified as having a high likelihoodof correctly representing the corresponding utterance. In oneembodiment, the questionable utterances and transcribed values can bestored separately for each voice stream or alternatively, thequestionable utterances and transcribed values can be stored for morethan one voice stream.

In a further embodiment, the voice utterance and assigned transcribedvalue can be added to the grammar, which is used during automated speechrecognition to assign initial transcribed values to the utterances inthe voice stream, as described above in FIG. 3. In yet a furtherembodiment, the voice utterance and assigned transcribed value are onlyadded to the grammar after the transcribed value has been assigned tothe utterance on a predetermined number of occasions. For instance, apredetermined number of occurrences can be set at two. Returning to theabove example, an utterance is initially assigned a transcribed value of“gaaata.” However, after additional analysis, the utterance is assigneda further transcribed value of “gotta.” A further utterance issubsequently received, which is the same as or similar to the previouslyreceived utterance. The further utterance is also assigned an initialtranscribed value of “gaaata.” Upon further analysis, the initialtranscribed value is replaced with a subsequent transcribed value“gotta.” Since the same or similar utterances have been twice assignedthe transcribed value “gotta,” the new transcribed value can then beadded to the grammar. Once added, the next time the same or similarutterance is received, the term “gotta” is initially assigned as thetranscribed value without having to undergo further analysis, such as bya human transcriber.

In yet a further embodiment, the voice utterance and assignedtranscribed value can be added to a secondary grammar. The addition of atranscribed value to a secondary grammar can be effective when thetranscribed value is not commonly used enough to warrant addition to themain grammar or when the addition of the transcribed value woulddeteriorate the effectiveness of a similar transcribed value on the maingrammar that is more important. Returning to the above example, insteadof being added to the grammar, the transcribed value “gotta” is assignedto a secondary grammar since the term “gotta” is slang for the phrase“got to.” The next time the same or similar utterance is received, theutterance is compared with the main grammar and then the secondarygrammar from which the term “gotta” is initially assigned as thetranscribed value without having to undergo further analysis, such as bya human transcriber. As described above, the assignment of thetranscribed value may be required to occur a particular number of timesbefore the transcribed value is added to the secondary grammar.Additional grammars are also possible, including third, fourth, andfifth grammars.

While the invention has been particularly shown and described asreferenced to the embodiments thereof, those skilled in the art willunderstand that the foregoing and other changes in form and detail maybe made therein without departing from the spirit and scope of theinvention.

What is claimed is:
 1. A computer-implemented system for efficientlyreducing transcription error during a call, comprising: at least oneprocessor; a group of similar questionable utterances, wherein eachquestionable utterance is assigned a transcribed value and is selectedfrom a different call; a sample module to select a sample of the similarquestionable utterances from the group and to provide the sample to ahuman transcriber; a transcription module to receive from the humantranscriber a further transcribed value for each of the similarquestionable utterances in the sample; a comparison module to comparethe further transcribed values for the similar questionable utterancesin the sample; and a delivery module to provide to the human transcribereach of the remaining similar questionable utterances in the group whenthe compared further transcribed values of the similar questionableutterances in the sample are different.
 2. A system according to claim1, further comprising: a group generation module to generate the groupof similar questionable utterances by identifying a questionableutterance, comparing other questionable utterances to the identifiedquestionable utterance, and designating as the group of similarquestionable utterances, the identified questionable utterance and theother questionable utterances each with a similarity to the identifiedquestionable utterance that satisfies a predetermined threshold.
 3. Asystem according to claim 2, further comprising: an identificationmodule to identify the other questionable utterances with similaritiesthat satisfy the predetermined threshold within a predetermined amountof time.
 4. A system according to claim 3, further comprising: adetermination module to determine the predetermined amount of time basedon at least one of whether the call is in real-time and a desiredtranscription accuracy.
 5. A system according to claim 2, furthercomprising: a similarity module to determine the similarity for each ofthe other questionable utterances based on at least one of initialtranscribed values, confidence scores, a range of similarity, and callcharacteristics.
 6. A system according to claim 1, further comprising:an assignment module to assign the further transcribed value to one suchsimilar questionable utterance in the sample when the transcribed valuefor that similar questionable utterance differs from the furthertranscribed value.
 7. A system according to claim 1, further comprising:a confirmation module to confirm the transcribed value assigned to onesuch similar questionable utterance in the sample when the furthertranscribed value is the same as the assigned transcribed value.
 8. Asystem according to claim 1, further comprising: a transcription moduleto assign the transcribed value to each similar questionable utteranceby recording a voice stream from each different call, parsing the voicestream from each different call into utterances, and selecting thetranscribed value for each utterances based on a grammar.
 9. A systemaccording to claim 8, further comprising: a message generation module tocombine the transcribed values for one such voice stream and to generatea transcribed message for that voice stream from the combinedtranscribed values.
 10. A system according to claim 8, furthercomprising: an identification module to identify at least one of theutterances for one such voice stream as the questionable utterances,comprising: an assignment module to assign a confidence score to each ofthe transcribed values for that voice stream; and a threshold module toidentify one or more of the utterances in the voice stream that havetranscribed values with confidence scores that fall below apredetermined threshold as the questionable utterances.
 11. Acomputer-implemented method for efficiently reducing transcription errorduring a call, comprising: monitoring a group of similar questionableutterances, wherein each questionable utterance is assigned atranscribed value and is selected from a different call; selecting asample of the similar questionable utterances from the group andproviding the sample to a human transcriber; receiving from the humantranscriber a further transcribed value for each of the similarquestionable utterances in the sample; comparing the further transcribedvalues for the similar questionable utterances in the sample; andproviding to the human transcriber each of the remaining similarquestionable utterances in the group when the compared furthertranscribed values of the similar questionable utterances in the sampleare different.
 12. A method according to claim 11, further comprising:generating the group of similar questionable utterances, comprising:identifying a questionable utterance; comparing other questionableutterances to the identified questionable utterance; and designating asthe group of similar questionable utterances, the identifiedquestionable utterance and the other questionable utterances each with asimilarity to the identified questionable utterance that satisfies apredetermined threshold.
 13. A method according to claim 12, furthercomprising: identifying the other questionable utterances withsimilarities that satisfy a predetermined threshold within thepredetermined amount of time.
 14. A method according to claim 13,further comprising: determining the predetermined amount of time basedon at least one of whether the call is in real-time and a desiredtranscription accuracy.
 15. A method according to claim 12, furthercomprising: determining the similarity for each of the otherquestionable utterances based on at least one of initial transcribedvalues, confidence scores, a range of similarity, and callcharacteristics.
 16. A method according to claim 11, further comprising:assigning the further transcribed value to one such similar questionableutterance in the sample when the transcribed value for that similarquestionable utterance differs from the further transcribed value.
 17. Amethod according to claim 11, further comprising: confirming thetranscribed value assigned to one such similar questionable utterance inthe sample when the further transcribed value is the same as theassigned transcribed value.
 18. A method according to claim 11, furthercomprising: assigning the transcribed value to each similar questionableutterance, comprising: recording a voice stream from each differentcall; parsing the voice stream from each different call into utterances;and selecting the transcribed value for each utterances based on agrammar.
 19. A method according to claim 18, further comprising:combining the transcribed values for one such voice stream; andgenerating a transcribed message for that voice stream from the combinedtranscribed values.
 20. A method according to claim 18, furthercomprising: identifying at least one of the utterances for one suchvoice stream as the questionable utterances, comprising: assigning aconfidence score to each of the transcribed values for that voicestream; and identifying one or more of the utterances in the voicestream that have transcribed values with confidence scores that fallbelow a predetermined threshold as the questionable utterances.